From NASA to AI Agents
This white paper examines how lessons from high-assurance operational systems can be applied to modern AI agents. It connects the disciplines behind mission planning, safety constraints, and accountable automation with the practical requirements of agentic systems operating in production.
What it covers
- Why deterministic control systems still matter in probabilistic AI environments.
- What AI teams can learn from mission-critical governance models.
- How policy, oversight, and runtime constraints reduce agent risk.
- Where structured knowledge and executable rules fit into agent design.
Why it matters
AI agents are increasingly expected to act across tools, data, and workflows with limited human intervention. That creates familiar problems: constraint handling, escalation, state awareness, and operational assurance. The same core questions that shaped earlier safety-critical systems now apply to agent platforms, orchestration layers, and enterprise deployment patterns.
Intended audience
- AI platform and product teams
- Governance, risk, and compliance leaders
- Technical programme owners
- Decision-makers evaluating agent deployment at scale